scholarly journals Improvement of Roller Bearing Diagnosis with Unlabeled Data Using Cut Edge Weight Confidence Based Tritraining

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Wei-Li Qin ◽  
Wen-Jin Zhang ◽  
Zhen-Ya Wang
Keyword(s):  
Fault Diagnosis ◽  
Roller Bearing ◽  
Unlabeled Data ◽  
Edge Weight ◽  
Ring Fault ◽  
Bearing Fault ◽  
Cut Edge ◽  
Bearing Fault Diagnosis ◽  
Roller Bearings ◽  
Diagnosis Method

Roller bearings are one of the most commonly used components in rotational machines. The fault diagnosis of roller bearings thus plays an important role in ensuring the safe functioning of the mechanical systems. However, in most cases of bearing fault diagnosis, there are limited number of labeled data to achieve a proper fault diagnosis. Therefore, exploiting unlabeled data plus few labeled data, this paper proposed a roller bearing fault diagnosis method based on tritraining to improve roller bearing diagnosis performance. To overcome the noise brought by wrong labeling into the classifiers training process, the cut edge weight confidence is introduced into the diagnosis framework. Besides a small trick called suspect principle is adopted to avoid overfitting problem. The proposed method is validated in two independent roller bearing fault experiment vibrational signals that both include three types of faults: inner-ring fault, outer-ring fault, and rolling element fault. The results demonstrate the desirable diagnostic performance improvement by the proposed method in the extreme situation where there is only limited number of labeled data.

Roller Bearing Fault Diagnosis Based on IMF Kurtosis and SVM

2013 ◽  
Vol 694-697 ◽  
pp. 1160-1166
Author(s):  
Ke Heng Zhu ◽  
Xi Geng Song ◽  
Dong Xin Xue
Keyword(s):  
Fault Diagnosis ◽  
Roller Bearing ◽  
Fault Classification ◽  
Support Vector ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Bearing Vibration ◽  
The Imf

This paper presents a fault diagnosis method of roller bearings based on intrinsic mode function (IMF) kurtosis and support vector machine (SVM). In order to improve the performance of kurtosis under strong levels of background noise, the empirical mode decomposition (EMD) method is used to decompose the bearing vibration signals into a number of IMFs. The IMF kurtosis is then calculated because of its sensitivity of impulses caused by faults. Subsequently, the IMF kurtosis values are treated as fault feature vectors and input into SVM for fault classification. The experimental results show the effectiveness of the proposed approach in roller bearing fault diagnosis.

A Bearing Fault Diagnosis Method Using Fusion of Lie Group Classifier and GA

2013 ◽  
Vol 790 ◽  
pp. 659-662
Author(s):  
Si Yuan Zhao ◽  
Wang Tao ◽  
Ge Xin ◽  
Yun Liu
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Lie Group ◽  
Rolling Bearing ◽  
Singular Value ◽  
Support Vector ◽  
Ring Fault ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

A novel bearing fault diagnosis method based on Lie group was proposed, and genetic algorithm (GA) was introduced to optimize feature amount. This method was applied to inner ring fault, outer ring fault and rolling element fault of rolling bearing. Firstly, the rolling bearing vibration signal was decomposed as intrinsic model functions (IMF) by using the empirical mode decomposition (EMD) method. The energy of every IMF and the singular value of the IMF matrix were chosen as features. The Shannon and Renyi entropy of the energy and singular value distribution were also extracted. Secondly genetic algorithm was used to reduce feature redundancy, with lowest classifier error rate and least feature amount as finess function. At last, a comparison was made between this method and least square support vector machine (LSSVM).The results showed that Lie group clkassifier was more sensitivce to feature. This method could use less feature amount to diagnose fault.

Fault Diagnostics Based on Pattern Spectrum Entropy and Proximal Support Vector Machine

2009 ◽  
Vol 413-414 ◽  
pp. 607-612 ◽  
Author(s):  
Xiang Tao Yu ◽  
Wen Xiu Lu ◽  
Fu Lei Chu
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Roller Bearing ◽  
Support Vector ◽  
Vibration Signals ◽  
Pattern Spectrum ◽  
Ring Fault ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Proximal Support Vector Machine

Based on pattern spectrum entropy and proximal support vector machine (PSVM), a motor rolling bearing fault diagnosis method is proposed in this paper. It is very difficult to filter the fault vibration signals from the strong noise background because the roller bearing fault diagnosis is a problem of multi-class classification of inner ring fault, outer ring fault and ball fault. Firstly, vibration signals are processed by the pattern spectrum. Secondly, the morphological pattern spectrum entropy, and pattern spectrum values are utilized to identify the fault features of input parameters of PSVM classifiers. The experiment results demonstrate that the pattern spectrum quantifies various aspects of the shape-size content of a signal, and PSVM costs a little time and has better efficiency than the standard SVM.

A roller bearing fault diagnosis method based on the improved ITD and RRVPMCD

Measurement ◽  
2014 ◽  
Vol 55 ◽  
pp. 255-264 ◽  
Author(s):  
Yu Yang ◽  
Haiyang Pan ◽  
Li Ma ◽  
Junsheng Cheng
Keyword(s):  
Fault Diagnosis ◽  
Roller Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

scholarly journals Rolling bearing fault diagnosis method based on VMD and LSSVM

2021 ◽  
Vol 1792 (1) ◽  
pp. 012035
Author(s):  
Xingtong Zhu ◽  
Zhiling Huang ◽  
Jinfeng Chen ◽  
Junhao Lu
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method
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